Tuning sound for infrastructures: artificial intelligence, automation, and the cultural politics of audio mastering.
This paper traces the infrastructural politics of automated music mastering to reveal how contemporary iterations of artificial intelligence (AI) shape cultural production. The paper examines the emergence of LANDR, an online platform that offers automated music mastering, built on top of supervised...
| Publicado en: | Cultural Studies Vol. 35; no. 4/5; pp. 750 - 771 |
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| Autores principales: | , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Jul-Sep2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=152096642&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 152096642 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 09502386 05G jtl: Cultural Studies issn: 09502386 maglogo: N pubinfo: dt: Jul-Sep2021 vid: 35 iid: 4/5 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 152096642 10.1080/09502386.2021.1895247 ppf: 750 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.5MB tig: atl: Tuning sound for infrastructures: artificial intelligence, automation, and the cultural politics of audio mastering. aug: au: Sterne, Jonathan Razlogova, Elena affil: Department of Art History and Communication Studies, McGill University, Montreal, Canada Department of History, Concordia University, Montreal, Canada su: Mastering (Sound recordings) Artificial intelligence Automation Cultural production Machine learning Technology & culture sug: subj: Mastering (Sound recordings) Artificial intelligence Automation Cultural production Machine learning Technology & culture keyword: culture and technology data machine learning music mastering ab: This paper traces the infrastructural politics of automated music mastering to reveal how contemporary iterations of artificial intelligence (AI) shape cultural production. The paper examines the emergence of LANDR, an online platform that offers automated music mastering, built on top of supervised machine learning branded as artificial intelligence. Increasingly, machine learning will become an integral part of signal processing for sounds and images, shaping the way media cultures sound, look, and feel. While LANDR is a product of the so-called 'big bang' in machine learning, it could not exist without specific conditions: specific kinds of commensurable data, as well as specific aesthetic and industrial conditions. Mastering, in turn, has become an indispensable but understudied part of music circulation as an infrastructural practice. Here we analyze the intersecting histories of machine learning and mastering, as well as LANDR's failure at automating other domains of audio engineering. By doing so, we critique the discourse of AI's inevitability and show the ways in which machine learning must frame or reframe cultural and aesthetic practices in order to automate them, in service of digital distribution, recognition, and recommendation infrastructures. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Cultural Studies is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Cultural Studies holder: Taylor & Francis Ltd dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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